You are strongly encouraged to typeset your homework submissions using LaTeX (if you do not know LaTeX yet, now is as good a time as any to learn it -- you will need it in grad school anyway). If you opt for handwritten submissions, make sure that they are legible -- if we cannot read it, we cannot grade it! All submissions will be done through Gradescope.
Multiple attempts will be allowed, but only your last submission before the deadline will be graded.
We reserve the right to take off points for not following directions.
Late submission policy:
Academic integrity: Feel free to discuss the assignment with each other in general terms,
and to search the Web for general guidance (not for complete solutions). All solutions should be written up
individually. If you make substantial use of some information from outside sources,
be sure to acknowledge the sources in your solution. At the first instance of cheating (copying from other students or unacknowledged sources on the Web), a grade of zero will be given for the assignment. At the second instance, you will automatically receive an F for the entire course.
Final presentation:
strikethrough.List of suggested papers:
C.E. Rohrs et al., Robustness of Continuous-Time Adaptive Control Algorithms in the Presence of Unmodeled Dynamics (1985)K.S. Narendra and K. Parthasarathy, Identification and Control of Dynamical Systems Using Neural Networks (1990) A. Delgado et al., Dynamic recurrent neural network for system identification and control (1995) A. Rantzer, A dual to Lyapunov's stability theorem (2001) A. Feuer and G.C. Goodwin, Linear deterministic adaptive control: fundamental limitations? (2003)E.D. Sontag, Adaptation and regulation with signal detection implies internal model (2003)J. Kwon and P. Mertikopoulos, A continuous-time approach to online optimization (2017)B.M. Jenkins et al., Convergence Properties of Adaptive Systems and the Definition of Exponential Stability (2018)J.E. Gaudio et al., Connections Between Adaptive Control and Optimization in Machine Learning (2019) F. Lu, et al., Model-free characterizations of the Hamilton-Jacobi-Bellman
equation and convex Q-learning in continuous time (2022)Course policies: